Is This a Joke? Detecting Humor in Spanish Tweets

While humor has been historically studied from a psychological, cognitive and linguistic standpoint, its study from a computational perspective is an area yet to be explored in Computational Linguistics. There exist some previous works, but a characterization of humor that allows its automatic recognition and generation is far from being specified. In this work we build a crowdsourced corpus of labeled tweets, annotated according to its humor value, letting the annotators subjectively decide which are humorous. A humor classifier for Spanish tweets is assembled based on supervised learning, reaching a precision of 84% and a recall of 69%.Comment: Preprint version, without referra

A Crowd-Annotated Spanish Corpus for Humor Analysis

Computational Humor involves several tasks, such as humor recognition, humor generation, and humor scoring, for which it is useful to have human-curated data. In this work we present a corpus of 27,000 tweets written in Spanish and crowd-annotated by their humor value and funniness score, with about four annotations per tweet, tagged by 1,300 people over the Internet. It is equally divided between tweets coming from humorous and non-humorous accounts. The inter-annotator agreement Krippendorff's alpha value is 0.5710. The dataset is available for general use and can serve as a basis for humor detection and as a first step to tackle subjectivity.Comment: Camera-ready version of the paper submitted to SocialNLP 2018, with a fixed typ

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research

Klasifikasi Teks Humor Bahasa Indonesia Memanfaatkan SVM

Dalam dunia humor banyak sekali ditemukan tipe tipe humor yang bervariasi namun dengan satu tujuan yaitu menghibur penikmat humor, klasifikasi terbesar adalah humor verbal dan non verbal, untuk humor non verbal inilah penelitian ini difokuskan, yaitu melakukan klasifikasi humor oneliner, humor sebaris barupa tulisan singkat yang bertujuan menghantarkan sebuah punchline dan premis dalam satu kalimat. Penelitian ini akan berusaha melakukan klasifikasi humor menjadi beberapa kategori dengan menggunakan algoritma SVM dan word2vec, klasifikasi ini nantinya diharapkan memisahkan jenis jenis humor oneliner menjadi beberapa tipe sesuai cara penyajiannya, dengan dataset yang didapat dari komedian komedian profesional dan dilakukan proses pengenalan manual oleh ahli di bidangnya, penelitian ini bertujuan menemukan beberapa kelas humor yang akan menjadi cikal bakal pengenalan komputerisasi humor berbahasa indonesia

On Sexual Lust as an Emotion

Sexual lust – understood as a feeling of sexual attraction towards another – has traditionally been viewed as a sort of desire or at least as an appetite akin to hunger. I argue here that this view is, at best, significantly incomplete. Further insights can be gained into certain occurrences of lust by noticing how strongly they resemble occurrences of “attitudinal” (“object-directed”) emotion. At least in humans, the analogy between the object-directed appetites and attitudinal emotions goes well beyond their psychological structure to include similar ways in which their occurrence can be introspectively recognized, resulting in similar extensions of their functionality and meaningfulness to the subject. I conclude that although further research is needed, given the strength of the analogy, the ability of lust to satisfy some general requirements for being an emotion, and perhaps certain neurological findings, lust may somewhat uniquely straddle the line between appetite and emotion

From humor recognition to Irony detection: The figurative language of social media

[EN] The research described in this paper is focused on analyzing two playful domains of language: humor and irony, in order to identify key values components for their automatic processing. In particular, we are focused on describing a model for recognizing these phenomena in social media, such as "tweets". Our experiments are centered on five data sets retrieved from Twitter taking advantage of user-generated tags, such as "#humor" and "#irony". The model, which is based on textual features, is assessed on two dimensions: representativeness and relevance. The results, apart from providing some valuable insights into the creative and figurative usages of language, are positive regarding humor, and encouraging regarding irony. (C) 2012 Elsevier B.V. All rights reserved.This work has been done in the framework of the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems and it has been partially funded by the European Commission as part of the WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People Framework, and by MICINN as part of the Text-Enterprise 2.0 project (TIN2009-13391-C04-03) within the Plan I + D + I. The National Council for Science and Technology (CONACyT - Mexico) has funded the research work of Antonio Reyes.Reyes Pérez, A.; Rosso, P.; Buscaldi, D. (2012). From humor recognition to Irony detection: The figurative language of social media. Data and Knowledge Engineering. 74:1-12. https://doi.org/10.1016/j.datak.2012.02.005S1127